The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. DATA SUMMARY: Canopy base height (CBH) describes the lowest point in a stand where there is sufficient available fuel (= .25 in dia.) to propagate fire vertically through the canopy. Specifically, CBH is defined as the lowest point at which the canopy bulk density is >= 0.012 kg m-3. A spatially explicit map of canopy base height supplies information used in fire behavior models such as FARSITE (Finney 1998) to determine the point at which a surface fire will transition to a crown fire. It should be noted that LANDFIRE layers will not include canopy characteristics in fuel types where the tree canopy is considered a part of the surface fuel and the surface fire behavior fuel model is chosen to reflect these conditions. This is because LANDFIRE assumes that the potential burnable biomass in the shorter tree canopies has been accounted for in the surface fuel model parameters. For example, maps of areas dominated by young or short conifer stands where the trees are represented by a shrub type fuel model will not include canopy characteristics. The map of canopy base height was generated using a predictive modeling approach to relate satellite imagery and spatially explicit environmental variables to CBH values calculated from field plots. CBH was calculated using a program developed by E. Reinhardt at the Missoula Fire Sciences Laboratory. Regression trees were used to link the calculated reference CBH to 30-meter Landsat satellite imagery and a series of 30-meter spatially explicit gradient layers representing climate, soil, topography, and biophysical phenomena, such as net and gross primary productivity. The models were built using the commercially available regression tree machine-learning algorithm Cubist (Quinlan 1993; Rulequest Research 2006). These models are spatially applied in the ERDAS Imagine image processing system. Users need to be aware that canopy base height values above 6 meters cannot be predicted with a reasonable level of accuracy because there are few stands with CBH > 6 meters, and it is therefore difficult to predict such high values with small sample sizes. The CBH data represented in this layer are continuous from 0 to 9.9 meters (to the nearest 0.1 meter). Some stands dominated by broadleaf species which typically do not permit initiation of crown fire (e.g. Populus spp.) are coded with a CBH of 10 meters. Since crown fire is rarely observed in most hardwood stands, the highest CBH value possible was used to prevent false simulation of crown fire in these areas. In addition, all CBH values >= 10 are binned into the thematic class of 100 (10 m * 10). All non - forest values, including herbaceous, and shrub systems and non-burnable types such as urban, barren, snow and ice and agriculture, were coded as 0. The time period for this data set is not applicable; that is, it is not possible to characterize this data set with a single date, nor is it logical to use a range.
